Page 348 - Contributed Paper Session (CPS) - Volume 6

CPS1958 Zaid AlQadhi et al.
integration between governments, private institutions and citizens (Choi et al,
2013). The information sharing significance across governmental entities
varies from sharing services, enhancing governmental services and end user
experience, supporting a decision aid tool, planning assets allocation, to
supporting emergency management systems.
As a pillar for the digital transformation journey, most federal entities in
the United Arab Emirates have established data management systems for their
assets and facilities, which in turn feed operational processes and support
decision makers. Moreover, this project is a great example of digital
transformation and Government Information Sharing initiatives within the UAE
governmental entities.
FEWA as the federal authority for electricity and water have an established
meters data management system which is quite resourceful in the amount of
details availed per meters, like meter type, location, activity, owner name,
owner nationality, unit land use, serial, etc. This meters data was part of
Government Information Sharing initiative where FCSA diverted the utilisation
of smart meters data to employ it in extracting household information and
farms enumeration in a cost effective approach while addressing the below
goals:
- Providing the distribution of household and farm density in the country
in the form of electronic maps and geospatial location.
- Providing up-to-date data for decision makers and policymakers.
- Analysing temporal household and farm density data to visualise annual
growth for decision makers.
- Providing interactive maps enriched with geospatial visualisation
techniques to give a better end user experience for the stakeholders.
- Presenting a resource efficient practice where the least resources were
acquired to implement a census project.

References
1. J. Choi, S.A. Chun, D.H. Kim, and A. Keromytis. (2013, June). SecureGov:
secure data sharing for government services. In Proceedings of the 14th
Annual International Conference on Digital Government Research 2013
Jun 17 (pp. 127-135)
2. K. Gajowniczek and T.Ząbkowski. (2015). Data Mining Techniques for
Detecting Household Characteristics Based on Smart Meter Data. In:
Energies 2015, 8, 7407-7427. Retrieved from;
https://www.mdpi.com/journal/energies

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